In a computing network environment, multiple computing resources are deployed at different geographical locations in order to execute jobs associated with software applications of different technologies. The execution of the jobs is managed through a service level agreement (SLA) between a service provider and a customer. The service provider facilitates the execution of the jobs belonging to the customer. The customer may prescribe its requirements regarding the execution of the jobs through the SLA. In one example, the customer may set a pre-defined time limit (also referred to as total processing time hereinafter) for the execution of the jobs. Based on the requirements of the customer, the service provider may have to accordingly plan and execute the jobs, so that the SLA is complied.
In order to comply with the SLA, the service provider may have to effectively analyze supply and demand of the computing resources, such that the jobs are executed within the pre-defined time limit set as per the SLA. Further, in a typical scenario, while there are first few set of jobs that are being processed for execution by the computing resources, the remaining set of jobs are waiting in a queue for the execution by the computing resources. In certain conditions, processing time required for execution of the first few set of jobs may vary from a predefined threshold time period as per the SLA. Since, there is variance in the execution of the first few set of jobs, it becomes a technical challenge to effectively plan and allocate the computing resources for the execution of the remaining set of jobs, such that the overall jobs including the first few set of jobs and the remaining set of jobs are executed in the total processing time as per the SLA.